The 5 Best Geospatial Packages to Use in Julia

Key Takeaways

• Julia is a powerful language for geospatial data science.
• There are a number of great geospatial packages available for Julia.
• The 5 packages discussed in this article are a great place to start for geospatial data handling.

Introduction Julia is a high-level, high-performance programming language that is becoming increasingly popular for geospatial data science. There are a number of great geospatial packages available for Julia, each with its own strengths and weaknesses. In this article, we will discuss the 5 best geospatial packages to use in Julia, and how to best use each of them.

1. GDAL.jl

What it does: GDAL.jl is a thin Julia wrapper for the GDAL library, which is a powerful open-source library for reading, writing, and manipulating geospatial data. GDAL.jl allows you to read and write a wide variety of geospatial data formats, including Shapefiles, GeoTIFFs, and KML files.

How to use it: GDAL.jl is easy to use. To read a Shapefile, for example, you would use the following code:

using GDAL 
shp = GDAL.open("my_shapefile.shp")

Once you have opened a geospatial dataset, you can use GDAL.jl to access its features, attributes, and metadata.

2. GeoDataFrames.jl

What it does: GeoDataFrames.jl is a Julia package that provides a geospatial extension to the popular DataFrames.jl package. GeoDataFrames.jl allows you to store geospatial data in a DataFrame, which makes it easy to manipulate and analyze this data.

How to use it: GeoDataFrames.jl is easy to use. To create a GeoDataFrame from a Shapefile, for example, you would use the following code:

using GeoDataFrames 
shp = read_shapefile("my_shapefile.shp") 
df = GeoDataFrame(shp)

Once you have created a GeoDataFrame, you can use it like any other DataFrame. You can access its features, attributes, and metadata, and you can use it to perform a variety of geospatial analysis tasks.

3. GeoJSON.jl

What it does: GeoJSON.jl is a Julia package that provides a parser and serializer…